Operating Without a Formula

Growth has no formula. Stop hunting for one.

Somewhere there is a course promising the seven steps to scale. A thread with the exact funnel that worked for someone else's company. A book that says do this first, then that. All of it sells because founders want a formula, and formulas are comforting: follow the steps, get the outcome. Growth does not work that way, and pretending it does is the first mistake this section asks you to drop.

Growth, personal or business, is entrepreneurial by nature. It depends on the person, their starting condition, where they want to go, and how the path fits them. Two founders can work on similar things from similar backgrounds and still walk different roads. Any method that presumes a universal sequence will fail some large share of the people it is applied to. That is not a flaw in the method. It is what happens when you apply a straight line to a problem that was never straight.

This is also why the field can outpace anyone's expertise and that is fine. When a market or technology moves faster than a person can master it, waiting to feel ready is a trap, because readiness never arrives. The move is to stop asking "do I know enough?" and start asking "how much do I want this, and how will we work together to get there?"

The explorer stance beats the knower stance whenever the field is moving faster than mastery is possible. An explorer does not wait for a map that matches the terrain exactly. An explorer goes in, adjusts, and reports back what is actually there.

Why the AI moment sharpens all of this

When AI drives execution cost toward zero, and anyone can produce work that used to take a skilled team, the scarce capability is not a specific craft. It is entrepreneurial skill itself: the ability to find clarity and direction inside abundance and chaos. That is the organizing idea behind this whole course, and it matters now more than it did five years ago, because the tools keep changing under everyone's feet. Content and curriculum built around today's tools go stale fast. What stays useful is how you think about the change, not which tool you used to ride it.

One more reframe removes a specific kind of anxiety. What makes something a startup is not how long it has existed on paper. It is the nature of the work: unvalidated, hypothesis-driven, with three unknowns stacked at once: does this solve the problem, will anyone pay for it, will they keep paying. A company can be years old and still be, functionally, a startup, because none of those three questions has been answered yet. And every company, no matter how large it becomes, spends a long stretch looking like an unproven pitch before it becomes an obviously real thing. Nobody, including the founders you admire, knew in advance what the "it's real now" moment would look like. You do not need to have it all figured out on day one. Almost nobody did.

Learn like an operator: on demand, from the source, only as deep as the problem requires.

The instinct when a field feels too big is to study harder. More reading, more courses, more tabs open. It does not work, because fields like this one do not have an edge you can reach. The fix is not more studying. It is changing what you are trying to do: swap "know a lot" for "have an approach."

1. Surrender to incompleteness first.Accept, explicitly, that you will never know everything about a fast-expanding space. Comparing it to "knowing everything about the internet" is close to accurate. This is not resignation. It is what makes the next steps feel legitimate instead of like cutting corners.
2. Hold surface knowledge on everything.Newsletters, headlines, a light regular pass. You need to know that something happened, not how it works underneath. That is enough for 90% of what crosses your desk.
3. Let interest or business need pull you deep.Go deep only where genuine curiosity pulls you, or where the business creates an actual requirement this week. Depth acquired for any other reason often does not transfer to the next problem anyway.
4. Apply the 80/20 task rule where you do go deep.Once you know the exact task, spend roughly 80% of your time on that task and 20% on the context around it. Resist the pull outward into the general subject. That pull is what turns a 30-minute task into a 6-hour rabbit hole.^*
5. Go to the person who owns the answer.When the open internet cannot resolve a specific, niche question, stop searching and find the person who actually knows: an engineer at the platform, a specialist practitioner. Bring prepared questions and get on a call. It turns an open-ended search into a bounded conversation.

It is common to try to "acquire knowledge" across a long list of topics a business needs, from accounting to software to marketing. The reframe that helps is not a study plan. It is a different question: what problem are you trying to solve, and how much of this subject do you actually need to solve it. You can become an accountant, or you can learn enough to brief an accountant, or you can learn enough to build one invoice. Three different learning scopes, and only one of them matches most weeks of actual work.

None of this argues for shallow work generally. It argues for spending your depth where it pays. A record of steps taken, mistakes made, and mistakes fixed fast beats expertise that anyone could Google in an afternoon. The moatWhatever makes your business hard for a competitor to copy., whatever makes you hard to copy, is not what you know. It is how far you have already walked and how quickly you corrected course along the way.

Data beats opinion. Pick your market deliberately.

Picture a founder spending two full days trying to open a business bank account, working through every bank the internet recommends as easy for a new company. Most of them turn out not to work for the actual situation: the wrong state, the wrong entity type, the wrong volume of transactions. Only the direct attempt, bank by bank, surfaces the one that actually works. That is the difference between collecting opinions and collecting data, and it scales into a standing operating question: how much data have we collected this week, where data means a direct attempt of your own, not aggregated sentiment from people who were not in your situation.

The same logic applies one level up, to picking a market rather than picking a task. When the same failure repeats across many independent, otherwise capable people, the honest diagnosis is that the system around them is failing, not that each of them individually chose badly. That reframe changes where you look for opportunity: not at who is struggling, but at why a whole population is struggling under the same structural conditions.

FrictionAnything that slows a customer down on the way to buying or using your product: extra steps, confusing pages, forms, waiting. itself is a signal worth reading correctly. Heavy friction, missing information, undocumented processes, usually reads as discouraging. It should read as the opposite. A mature market has abundant information and low friction precisely because it is already figured out and crowded. High friction tells you the market is early, which is where the room to work actually is. Pick a market where your customer's world moves slower than the underlying technology does. You cannot outrun a well-funded platform at the center of a fast-moving space, but you can dominate a customer segment that space has not gotten around to yet.

One more filter, for when the market itself gets loud with public skepticism. Criticism is free to produce. A video mocking a category costs its author nothing. What costs something, and therefore carries real signal, is where sophisticated, well-resourced investors actually put their money, especially during a category's worst stretch. That does not make the criticism invalid. It makes it weaker evidence than capital that was actually risked.